West Midlands Fire Service
Animal Rescues

Data exploration and initial insights

Jurgen Mitsch

2023-07-14

Incident Data - Overview

Data Characteristics

  • Date range: Apr 2013 - Mar 2023
  • 1411 Total animal rescue incidents
  • 7 Districts, 190 Wards in the West Midlands

Key Observations

  • Annual total nearly doubled over time
    13/14 (99) to 22/23 (185)
  • Notable increases in 14/15, 16/17, and 20/21
  • Incidents are infrequent day to day
  • Frequency of days with incidents has increased
    (24% in 13/14 to 40% in 22/23)
  • Annual seasonality
Figure 1: Total animal rescue incidents by financial year (top) and by calendar week (bottom).

Animal Types

Key observations

  • Predominantly cat and dog incidents (64%)

  • Cat incidents:

    • account for 57% of total increase
      (since 13/14)
    • have increased by 37.5%
      (since 19/20)
  • Dog-related incidents dropped in 20/21 but have since returned to 19/20 levels

  • Data on pet population over time somewhat ambiguous

  • Ca. 20% of incident descriptions mention RSPCA prior to 17/18 but then drop to 0
  • Analysing incident descriptions:
    • Cats getting stuck on trees or roofs
    • Dogs getting stuck in gates and fences
    • Birds getting stuck in fishing gear

incidents by animal type over time

Figure 3: West Midlands Fire Service animal rescue incidents: Total incidents broken down by year and animal type. Total incidents by financial year (left) and overall breakdown across all years (right) shown.

Incident Location

Key observations

  • Birmingham
    • accounts for ca. 38% of all incidents (2013-2023)
    • contributes ca. 47% of incident increase since 2013
  • Highest incident ratios per 10,000 people:(since 20/21, population from 2021 census)
    • Dudley (0.78)
    • Walsall (0.7)
  • Data very sparse at Ward level, difficult to interpret
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Figure 4: District level visualisation of incident and population geography.
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Figure 5: Ward level visualisation of incident and population geography.

Where next?

What’s the question?

Stakeholder input is crucial for:

  • defining the ask
  • interpretation of data and context
  • avoiding ‘number blindness’
  • steering, prioritising, and reviewing progress (AGILE)

Example questions:

  • What factors might impact observed trends?
  • Empirical ‘thresholds of significance’ for incident count or frequency?
  • Are Fire Services the only entity responding to animal rescue incidents?
  • Which fire service team(s) can respond?
  • Data quality considerations?
  • More data? - depending on the question!
    • ‘Unresponded’ incidents or ‘false alarms’?
    • ‘Call source’ and response times?
    • What happens to the animals afterwards?
    • Fire station / response team locations?

Parallels to Healthcare

  • Ambulance Services
  • Interaction of system-level services and organisaitons
    • Discharge pathways and destinations
    • Admission routes to healthcare providers
    • Modelling of similarly sparse services/treatment pathways
  • Health Inequalities
    • Access to healthcare services
    • Geographic / demographic split
    • Prevalence of conditions/comorbidities

Appendix

Year on year change of incident totals

Figure 6: Year on year change in total incidents by financial year (Mar-Apr).

Total incidents by month

Figure 7: Total animal rescue incidents by month (blue) and 12 month rolling mean (red dashed).

Incidents by weekday

Figure 8: West Midlands Fire Service animal rescue incidents broken down by weekday

Time between incidents

Figure 9: Time between incidents in days

Ratio of days in the (financial) year with incidents

Figure 10: Ratio [%] of days in each financial year where animal rescue incidents have occured.

Incidents by district over time and animal type

Figure 11: Total Incidents by financial year by district (left) and Birmingham incidents by animal type (right).

Animal Populations over time

Figure 12: Cat, dog, and rabbit population over time in millions - Source: PDSA PAW Report